- #CRASHES CRYPTOCURRENCY FREEFALL ACCELERATES DRIVERS#
- #CRASHES CRYPTOCURRENCY FREEFALL ACCELERATES SERIES#
īesides interest in the purely statistical properties of the Bitcoin financial time series, there has been growing focus on the social component shaping Bitcoin price dynamics. Other approaches to econometric Bitcoin modelling are outlined in. Donier & Bouchaud investigated Bitcoin liquidity based on order book data and, from this, accurately predicted the size of price crashes.
determined even larger tail risk than usually seen in stocks. Furthermore, Osterrieder & Lorenz found much larger magnitude in the heavy tail of the Bitcoin return distribution compared to conventional currencies. Bariviera provided evidence for volatility clustering through a long memory Hurst exponent analysis. The hedging properties against other asset classes were investigated by Bouri et al. Urquhart & Zhang studied a variety of GARCH volatility models and tested the hedging capability of the crypto-coin against other currencies. Pichl & Kaizoji modelled the time-varying realized volatility of Bitcoin and found it to be significantly larger compared to that of fiat currencies. There have already been a number of studies examining the statistical properties of Bitcoin returns. Nevertheless, cryptocurrencies, and especially Bitcoin as a precursor of this new asset class, have drawn increased attention by the scientific and investor communities, due to the strong growth of the sector over the past years as well as the promising technological and economic prospects. The goal of this study is to document these bubbles and crashes, put them into a historical perspective and analyse their predictability.Īt the time of writing, the combined capitalization of all existing cryptocurrencies still amounts to less than 1% of the world GDP, a fact illustrating the still low significance of this market in the global economic context. In fact, as we will demonstrate in this paper, multiple overlapping short- and long-term Bitcoin price bubbles have appeared between 20. Introduced in 2008, Bitcoin started trading on organized markets in 2010 and, from the beginning, has exhibited a turbulent market history such that the bubble culminating in December 2017 does not appear to be that exceptional. The massive crash was preceded by a no less impressive 43 times price boost (over 730 days before the peak in mid-December 2017). For instance, as a consequence of the crash following mid-December 2017, a book-to-market value of more than 200 billion US Dollars of Bitcoin’s total market capitalization evaporated within only six weeks, resulting in a cumulative loss from the peak of 41% (over 42 trading days after the peak that occurred in mid-December 2017). Overall, our predictive scheme provides useful information to warn of an imminent crash risk.įrom an investment point of view, during the past decade, Bitcoin has become known for two main reasons: its extraordinary return potential in phases of extreme price growth as well as regular massive crashes of the cryptocurrency. We present these predictions for the three long bubbles and the four short bubbles that our time scale of analysis was able to resolve. Each cluster is proposed as a plausible scenario for the subsequent Bitcoin price evolution. Furthermore, for various fictitious ‘present’ times t 2 before the crashes, we employ a clustering method to group the predicted critical times t c of the LPPLS fits over different time scales, where t c is the most probable time for the ending of the bubble. Then, a detailed analysis of the growing risks associated with the three long bubbles using the Log-Periodic Power-Law Singularity (LPPLS) model is based on the LPPLS Confidence Indicators, defined as the fraction of qualified fits of the LPPLS model over multiple time windows.
#CRASHES CRYPTOCURRENCY FREEFALL ACCELERATES DRIVERS#
We explain this classification of long and short bubbles by a number of quantitative metrics and graphs to understand the main socio-economic drivers behind the ascent of Bitcoin over this period. In combination with the Lagrange Regularization Method for detecting the beginning of a new market regime, we identify three major peaks and 10 additional smaller peaks, that have punctuated the dynamics of Bitcoin price during the analysed time period.
#CRASHES CRYPTOCURRENCY FREEFALL ACCELERATES SERIES#
We introduce a robust automatic peak detection method that classifies price time series into periods of uninterrupted market growth (drawups) and regimes of uninterrupted market decrease (drawdowns). We present a detailed bubble analysis of the Bitcoin to US Dollar price dynamics from January 2012 to February 2018.
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